Abstract
Purpose
Adolescent idiopathic scoliosis is a chronic disease that may require correction surgery. The finite element method (FEM) is a popular option to plan the outcome of surgery on a patient-based model. However, it requires considerable computing power and time, which may discourage its use. Machine learning (ML) models can be a helpful surrogate to the FEM, providing accurate real-time responses. This work implements ML algorithms to estimate post-operative spinal shapes.
Methods
The algorithms are trained using features from 6400 simulations generated using the FEM from spine geometries of 64 patients. The features are selected using an autoencoder and principal component analysis. The accuracy of the results is evaluated by calculating the root-mean-squared error and the angle between the reference and predicted position of each vertebra. The processing times are also reported.
Results
A combination of principal component analysis for dimensionality reduction, followed by the linear regression model, generated accurate results in real-time, with an average position error of 3.75 mm and orientation angle error below 2.74 degrees in all main 3D axes, within 3 ms. The prediction time is considerably faster than simulations based on the FEM alone, which require seconds to minutes.
Conclusion
It is possible to predict post-operative spinal shapes of patients with AIS in real-time by using ML algorithms as a surrogate to the FEM. Clinicians can compare the response of the initial spine shape of a patient with AIS to various target shapes, which can be modified interactively. These benefits can encourage clinicians to use software tools for surgical planning of scoliosis.
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References
Assi KC, Labelle H, Cheriet F (2014) Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning. Comput Biol Med 48:85–93
Cheng JC, Castelein RM, Chu WC, Danielsson AJ, Dobbs MB, Grivas TB, Gurnett CA, Luk KD, Moreau A, Newton PO (2015) Adolescent idiopathic scoliosis. Nat Rev Dis Primers 1(1):1–21
Jalalian A, Gibson I, Tay EH (2013) Computational biomechanical modeling of scoliotic spine: challenges and opportunities. Spine Deform 1(6):401–411
Dabiri Y, Velden AVD, Sack KL, Choy JS, Kassab GS, Guccione J (2019) Prediction of left ventricular mechanics using machine learning. Front Phys 7:117
Pellicer-Valero OJ, Rupérez MJ, Martínez-Sanchis S, Martín-Guerrero JD (2020) Real-time biomechanical modeling of the liver using machine learning models trained on finite element method simulations. Expert Syst Appl 143:113083
Jahya A, Herink M, Misra S (2013) A framework for predicting three-dimensional prostate deformation in real time. Int J Med Robot Comput Assist Surg 9(4):52–60
García-Cano E, Cosío FA, Duong L, Bellefleur C, Roy-Beaudry M, Joncas J, Parent S, Labelle H (2018) Prediction of spinal curve progression in adolescent idiopathic scoliosis using random forest regression. Comput Biol Med 103:34–43
Deschênes S, Charron G, Beaudoin G, Labelle H, Dubois J, Miron M-C, Parent S (2010) Diagnostic imaging of spinal deformities: reducing patients radiation dose with a new slot-scanning x-ray imager. Spine 35(9):989–994
Taleghani E, Singh A, Hachem B, Benoit D, Rustagi R, Vithoulkas G, Mac-Thiong J-M, Syed H (2021) Finite element assessment of a disc-replacement implant for treating scoliotic deformity. Clin Biomech 84:105326
Gardner-Morse MG, Stokes IA (2004) Structural behavior of human lumbar spinal motion segments. J Biomech 37(2):205–212
Oxland TR, Lin R-M, Panjabi MM (1992) Three-dimensional mechanical properties of the thoracolumbar junction. J Orthop Res 10(4):573–580
Hinton GE, Zemel RS (1994) Autoencoders, minimum description length and Helmholtz free energy. Adv Neural Inf Process Syst 6:3–10
Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52
Chollet F (2021) Deep Learning with Python. Simon and Schuster, Shelter Island
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine Learning in Python. J Mach Learn Res 12:2825–2830
Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2):229–244
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Acknowledgements
This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC); Engage Grants under Grant 543780-19; Spinologics Inc, and the NVIDIA GPU grant program. The Titan X Graphics Card used for this research was donated by the NVIDIA Corporation.
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The authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article. Bahe Hachem, Julien Clin, and Jean-Marc Mac-Thiong are employed by Spinologics Inc.
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Phellan Aro, R., Hachem, B., Clin, J. et al. Real-time prediction of postoperative spinal shape with machine learning models trained on finite element biomechanical simulations. Int J CARS 19, 1983–1990 (2024). https://doi.org/10.1007/s11548-024-03237-5
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DOI: https://doi.org/10.1007/s11548-024-03237-5